EAI Endorsed Transactions on Internet of Things https://publications.eai.eu/index.php/IoT <p>EAI Endorsed Transactions on Internet of Things is open access, a peer-reviewed scholarly journal focused on all areas related to the technologies and application fields related to the Internet of Things. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications on a quarterly frequency. Authors are not charged for article submission and processing.</p> en-US <p>This is an open-access article distributed under the terms of the Creative Commons Attribution <a href="https://creativecommons.org/licenses/by/3.0/" target="_blank" rel="noopener">CC BY 3.0</a> license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.</p> publications@eai.eu (EAI Publications Department) publications@eai.eu (EAI Support) Thu, 23 Mar 2023 11:31:00 +0000 OJS http://blogs.law.harvard.edu/tech/rss 60 Smartagb: Aboveground Biomass Estimation of Sorghum Based on Spatial Resolution, Machine Learning and Vegetation Index https://publications.eai.eu/index.php/IoT/article/view/2904 <p class="ICST-abstracttext"><span lang="EN-GB">This work aims to explore the feasibility of predicting and estimating the aboveground biomass (AGB) of sorghum using multispectral images captured by UAVs, and clarify the quantitative relationship between vegetation index and sorghum AGB based on different spatial resolutions, and build an AGB estimation model based on UAV multispectral images and vegetation index under different spatial resolutions. Combining spatial resolution, vegetation index, and machine learning, a training set is used to train the model, and a verification set is used to verify the model to select the best prediction model corresponding to different spatial resolutions. The three best prediction models under three spatial resolutions are classic machine learning models. 1) when the spatial resolution is 0.017m, the model precision obtained from the random forest is R2=0.8961, MAE=26.4340, and RMSE=32.2459. 2) when the spatial resolution is 0.024m, the model accuracy obtained by the Lasso algorithm is R2=0.8826, MAE=31.106, and RMSE=40.2937; 3) when the spatial resolution is 0.030m, the model accuracy obtained by the decision tree algorithm is R2=0.8568, MAE=30.3373, and RMSE=40.8082; and 4) the model's accuracy decreases with the decrease of spatial resolution. The results show that the combination of spatial resolution, vegetation index, and machine learning algorithm is an effective, fast, and accurate prediction method.</span></p> Qi Liu, Yaxin Wang, Jie Yang, Wuping Zhang, Huanchen Wang, Fuzhong Li, Guofang Wang, Yuansen Huo, Jiwan Han Copyright (c) 2023 EAI Endorsed Transactions on Internet of Things https://creativecommons.org/licenses/by/3.0/ https://publications.eai.eu/index.php/IoT/article/view/2904 Thu, 23 Mar 2023 00:00:00 +0000 Avoiding Congestion for Coap Burst Traffic https://publications.eai.eu/index.php/IoT/article/view/2655 <p class="ICST-abstracttext"><span lang="EN-GB">Congestion is an important issue in Internet of Things (IoT) networks with constrained devices and a growing number of applications. This paper investigated the problem of congestion control for burst traffic in such networks. We highlight the shortcomings of the current constrained application protocol (CoAP) in its inability to support burst traffic and rate control. Subsequently, we propose an analytical model for CoAP burst traffic and a new rate-control algorithm for CoAP to avoid congestion. A CoAP sender increases or decreases the transmission rate depending on the congestion detection. Using simulations, we compared the performance of the proposed algorithm with the current CoAP in various traffic scenarios. Experimental results show that the proposed algorithm is efficient for burst traffic and provides better performance in terms of delay, throughput, retransmission, packet duplication, and packet loss compared to CoAP.</span></p> Thi Thuy Duong Le, Dang Hai Hoang, Thieu Nga Pham Copyright (c) 2023 EAI Endorsed Transactions on Internet of Things https://creativecommons.org/licenses/by/3.0/ https://publications.eai.eu/index.php/IoT/article/view/2655 Wed, 29 Mar 2023 00:00:00 +0000 Volunteered Geographic Information (VGI) in Spatial Data Infrastructure (SDI) Continuum https://publications.eai.eu/index.php/IoT/article/view/2979 <p class="ICST-abstracttext"><span lang="EN-GB">Spatial data infrastructure (SDI) is a system that supports the management and use of geospatial data and related resources. It involves the creation and maintenance of a network of organizations, people, and technology that enables the sharing of geospatial data across sectors and stakeholders. In recent years, the growth of geospatial data and the increasing reliance on it by various sectors has led to the emergence of new trends in SDI, such as the use of cloud computing and big data analytics, the integration of geospatial data with other types of data, and the emphasis on open data and data interoperability. Volunteered geographic information (VGI) refers to geospatial data that is collected and contributed by individuals or groups, rather than traditional sources using the application of web 2.0 and location based applications, social media, mobile devices or say citizens as the censors. Crowdsourcing in geospatial data generation concept of VGI has changed the traditional concept of SDI having one way relationship as producers and users to the user driven SDI, where user create diverse, high quality data (spatial, temporal, attribute) and also use the data interoperable, transparently, world widely and free of cost. Various authors have discussed about the application of VGI in the world of the digital data and also point outs the possibility of integration of VGI in SDI as the starting of the new generation of SDI in the form of Global GIS platform, Data Spaces, System of Systems (SoS), Geoverse, Digital Earth, Digital Twin, Virtual Geographic Environment (VGEs). However, there exists multiple VGI challenges such as data quality, data structure, data differentiation, data copyright, and data confidentiality and privacy, but with the proper cooperation and partnerships, policy and legal arrangements, standard developments, financial arrangements, inter/intra communication and added advantages of web 3.0, concept of Global Digital Ecosystem containing Geoverse, SDI and SoS is possible. Hence, VGI is the present and also the future in this SDI continuum.</span></p> Kamal Shahi Copyright (c) 2023 EAI Endorsed Transactions on Internet of Things https://creativecommons.org/licenses/by/3.0/ https://publications.eai.eu/index.php/IoT/article/view/2979 Mon, 29 May 2023 00:00:00 +0000